{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Production Problem Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0), (200, 400), (500, 700), (1000, 900), (1500, 1500)]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs_1 = [(0, 0), (1000, 400), (1500, 1300)]\n",
    "costs_2 = [(0, 0), (300, 300), (700, 500), (1200, 600), (1500, 1100)]\n",
    "costs_3 = [(0, 0), (200, 400), (500, 700), (1000, 900), (1500, 1500)]\n",
    "\n",
    "\n",
    "def scale_up(l):\n",
    "    return [(100 * x[0], 100 * x[1]) for x in l]\n",
    "\n",
    "\n",
    "# costs_1 = scale_up(costs_1)\n",
    "# costs_2 = scale_up(costs_2)\n",
    "# costs_3 = scale_up(costs_3)\n",
    "costs_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (1000, 400), (1500, 1300)]\n",
      "[(0, 0), (300, 300), (700, 500), (1200, 600), (1500, 1100)]\n",
      "[(0, 0), (200, 400), (500, 700), (1000, 900), (1500, 1500)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_468786/2953457985.py:6: RankWarning: Polyfit may be poorly conditioned\n",
      "  coefficients = np.polyfit(x, y, n)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def plot_cost_component(cost_component, ax, label, color):\n",
    "    print(cost_component)\n",
    "    x, y = [x for x, y in cost_component], [y for x, y in cost_component]\n",
    "    n = 4\n",
    "    coefficients = np.polyfit(x, y, n)\n",
    "\n",
    "    # Define the function\n",
    "    def f(x: int) -> float:\n",
    "        return sum(c * x ** (n - i) for i, c in enumerate(coefficients))\n",
    "\n",
    "    x = [i for i in range(1500)]\n",
    "    y = [f(i) for i in x]\n",
    "    ax.plot(x, y, label=label, color=color)\n",
    "    ax.plot(\n",
    "        [x for x, y in cost_component],\n",
    "        [y for x, y in cost_component],\n",
    "        \":o\",\n",
    "        color=color,\n",
    "    )\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.title(\"Costs of bulk orders for different components\")\n",
    "plot_cost_component(costs_1, ax, \"Component 1\", \"blue\")\n",
    "plot_cost_component(costs_2, ax, \"Component 2\", \"green\")\n",
    "plot_cost_component(costs_3, ax, \"Component 3\", \"orange\")\n",
    "\n",
    "ax.set_xlabel(\"Size of bulk order (x)\")\n",
    "ax.set_ylabel(\"Cost of bulk order (f(x))\")\n",
    "# plt.plot([x for x, y in cost_component_1], [y for x, y in cost_component_1], 'bo-')\n",
    "# plt.plot([x for x, y in cost_component_2], [y for x, y in cost_component_2], 'o-', color='orange')\n",
    "# plt.plot([x for x, y in cost_component_3], [y for x, y in cost_component_3], 'go-')\n",
    "\n",
    "plt.grid()\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"production_example_cost_components.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (100, 800), (200, 1600), (300, 2000)]\n",
      "[(0, 0), (80, 1000), (150, 1300), (200, 1400), (300, 1500)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_468786/2953457985.py:6: RankWarning: Polyfit may be poorly conditioned\n",
      "  coefficients = np.polyfit(x, y, n)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gain_1 = [(0, 0), (100, 800), (200, 1600), (300, 2_000)]\n",
    "gain_2 = [(0, 0), (80, 1_000), (150, 1_300), (200, 1_400), (300, 1_500)]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.title(\"Selling price of products\")\n",
    "plot_cost_component(gain_1, ax, \"Product 1\", \"blue\")\n",
    "plot_cost_component(gain_2, ax, \"Product 2\", \"green\")\n",
    "plt.xlim(0, 305)\n",
    "plt.ylim(0, 2_700)\n",
    "plt.grid()\n",
    "plt.xlabel(\"Number of produced items\")\n",
    "plt.ylabel(\"Cumulative selling price of produced items\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"production_example_selling_price.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Starting CP-SAT solver v9.9.3963\n",
      "Parameters: log_search_progress: true\n",
      "Setting number of workers to 16\n",
      "\n",
      "Initial optimization model '': (model_fingerprint: 0x70af42a01e0ba820)\n",
      "#Variables: 16 (#ints: 5 in objective)\n",
      "  - 6 Booleans in [0,1]\n",
      "  - 2 in [0,300]\n",
      "  - 1 in [0,1100]\n",
      "  - 1 in [0,1300]\n",
      "  - 5 in [0,1500]\n",
      "  - 1 in [0,2000]\n",
      "#kExactlyOne: 2 (#literals: 6)\n",
      "#kLinear1: 26 (#enforced: 16)\n",
      "#kLinear2: 22 (#enforced: 8)\n",
      "#kLinear3: 3\n",
      "\n",
      "Starting presolve at 0.00s\n",
      "  2.45e-05s  0.00e+00d  [DetectDominanceRelations] \n",
      "  1.87e-04s  0.00e+00d  [PresolveToFixPoint] #num_loops=2 #num_dual_strengthening=1 \n",
      "  3.72e-06s  0.00e+00d  [ExtractEncodingFromLinear] #potential_supersets=2 \n",
      "[Symmetry] Graph for symmetry has 75 nodes and 104 arcs.\n",
      "[Symmetry] Symmetry computation done. time: 3.9864e-05 dtime: 1.054e-05\n",
      "  2.58e-04s  1.87e-05d  [Probe] #probed=28 #new_binary_clauses=4 \n",
      "  3.65e-06s  0.00e+00d  [MaxClique] \n",
      "  1.70e-05s  0.00e+00d  [DetectDominanceRelations] \n",
      "  1.05e-04s  0.00e+00d  [PresolveToFixPoint] #num_loops=1 #num_dual_strengthening=1 \n",
      "  1.29e-05s  0.00e+00d  [ProcessAtMostOneAndLinear] \n",
      "  4.78e-05s  0.00e+00d  [DetectDuplicateConstraints] #duplicates=5 \n",
      "  1.57e-05s  1.23e-07d  [DetectDominatedLinearConstraints] #relevant_constraints=11 #num_inclusions=8 \n",
      "  3.61e-06s  0.00e+00d  [DetectDifferentVariables] \n",
      "  8.20e-06s  1.80e-08d  [ProcessSetPPC] #relevant_constraints=2 \n",
      "  1.16e-06s  0.00e+00d  [FindAlmostIdenticalLinearConstraints] \n",
      "  8.03e-06s  8.10e-07d  [FindBigAtMostOneAndLinearOverlap] \n",
      "  4.21e-06s  1.01e-06d  [FindBigVerticalLinearOverlap] \n",
      "  1.52e-06s  0.00e+00d  [FindBigHorizontalLinearOverlap] \n",
      "  2.60e-06s  0.00e+00d  [MergeClauses] \n",
      "  1.69e-05s  0.00e+00d  [DetectDominanceRelations] \n",
      "  9.17e-05s  0.00e+00d  [PresolveToFixPoint] #num_loops=1 #num_dual_strengthening=1 \n",
      "  1.34e-05s  0.00e+00d  [DetectDominanceRelations] \n",
      "  8.52e-05s  0.00e+00d  [PresolveToFixPoint] #num_loops=1 #num_dual_strengthening=1 \n",
      "[Symmetry] Graph for symmetry has 70 nodes and 94 arcs.\n",
      "[Symmetry] Symmetry computation done. time: 1.5319e-05 dtime: 8.76e-06\n",
      "  1.22e-04s  1.81e-05d  [Probe] #probed=28 #new_binary_clauses=4 \n",
      "  1.48e-06s  0.00e+00d  [MaxClique] \n",
      "  1.02e-05s  0.00e+00d  [DetectDominanceRelations] \n",
      "  5.48e-05s  0.00e+00d  [PresolveToFixPoint] #num_loops=1 #num_dual_strengthening=1 \n",
      "  6.52e-06s  0.00e+00d  [ProcessAtMostOneAndLinear] \n",
      "  2.04e-05s  0.00e+00d  [DetectDuplicateConstraints] \n",
      "  7.75e-06s  1.23e-07d  [DetectDominatedLinearConstraints] #relevant_constraints=11 #num_inclusions=8 \n",
      "  2.22e-06s  0.00e+00d  [DetectDifferentVariables] \n",
      "  3.89e-06s  1.80e-08d  [ProcessSetPPC] #relevant_constraints=2 \n",
      "  6.51e-07s  0.00e+00d  [FindAlmostIdenticalLinearConstraints] \n",
      "  3.23e-06s  8.10e-07d  [FindBigAtMostOneAndLinearOverlap] \n",
      "  2.52e-06s  1.01e-06d  [FindBigVerticalLinearOverlap] \n",
      "  7.01e-07s  0.00e+00d  [FindBigHorizontalLinearOverlap] \n",
      "  6.91e-07s  0.00e+00d  [MergeClauses] \n",
      "  7.65e-06s  0.00e+00d  [DetectDominanceRelations] \n",
      "  4.42e-05s  0.00e+00d  [PresolveToFixPoint] #num_loops=1 #num_dual_strengthening=1 \n",
      "  4.75e-06s  0.00e+00d  [ExpandObjective] #entries=12 #tight_variables=6 #tight_constraints=2 \n",
      "\n",
      "Presolve summary:\n",
      "  - 0 affine relations were detected.\n",
      "  - rule 'TODO dual: only one blocking constraint?' was applied 42 times.\n",
      "  - rule 'TODO dual: only one unspecified blocking constraint?' was applied 30 times.\n",
      "  - rule 'deductions: 10 stored' was applied 1 time.\n",
      "  - rule 'duplicate: merged rhs of linear constraint' was applied 2 times.\n",
      "  - rule 'duplicate: removed constraint' was applied 5 times.\n",
      "  - rule 'linear: always true' was applied 21 times.\n",
      "  - rule 'linear: simplified rhs' was applied 30 times.\n",
      "  - rule 'presolve: 0 unused variables removed.' was applied 1 time.\n",
      "  - rule 'presolve: iteration' was applied 2 times.\n",
      "\n",
      "Presolved optimization model '': (model_fingerprint: 0xce27b532fa61ea6b)\n",
      "#Variables: 16 (#ints: 5 in objective)\n",
      "  - 6 Booleans in [0,1]\n",
      "  - 2 in [0,300]\n",
      "  - 1 in [0,1100]\n",
      "  - 1 in [0,1300]\n",
      "  - 5 in [0,1500]\n",
      "  - 1 in [0,2000]\n",
      "#kExactlyOne: 2 (#literals: 6)\n",
      "#kLinear1: 6 (#enforced: 6)\n",
      "#kLinear2: 16 (#enforced: 8)\n",
      "#kLinear3: 3\n",
      "\n",
      "Preloading model.\n",
      "#Bound   0.00s best:-inf  next:[-3900,3500] initial_domain\n",
      "[Symmetry] Graph for symmetry has 70 nodes and 94 arcs.\n",
      "[Symmetry] Symmetry computation done. time: 1.2102e-05 dtime: 8.76e-06\n",
      "#Model   0.00s var:16/16 constraints:27/27\n",
      "\n",
      "Starting search at 0.00s with 16 workers.\n",
      "11 full problem subsolvers: [core, default_lp, lb_tree_search, max_lp, no_lp, objective_lb_search, probing, pseudo_costs, quick_restart, quick_restart_no_lp, reduced_costs]\n",
      "4 first solution subsolvers: [fj_long_default, fj_short_default, fs_random, fs_random_quick_restart]\n",
      "9 incomplete subsolvers: [feasibility_pump, graph_arc_lns, graph_cst_lns, graph_dec_lns, graph_var_lns, rins/rens, rnd_cst_lns, rnd_var_lns, violation_ls]\n",
      "3 helper subsolvers: [neighborhood_helper, synchronization_agent, update_gap_integral]\n",
      "#1       0.00s best:-1202 next:[-1201,3500] no_lp (fixed_bools=0/20)\n",
      "#Bound   0.00s best:-1202 next:[-1201,3260] default_lp\n",
      "#Bound   0.00s best:-1202 next:[-1201,2390] max_lp\n",
      "#2       0.00s best:-1201 next:[-1200,2390] no_lp (fixed_bools=0/22)\n",
      "#3       0.00s best:-1200 next:[-1199,2390] no_lp (fixed_bools=0/22)\n",
      "#4       0.00s best:-1189 next:[-1188,2390] no_lp (fixed_bools=0/28)\n",
      "#5       0.00s best:-1    next:[-0,2390]  no_lp (fixed_bools=0/31)\n",
      "#6       0.00s best:665   next:[666,2390] default_lp (fixed_bools=0/26)\n",
      "#7       0.00s best:667   next:[668,2390] default_lp (fixed_bools=0/34)\n",
      "#8       0.00s best:964   next:[965,2390] default_lp (fixed_bools=0/40)\n",
      "#Model   0.01s var:15/16 constraints:25/27\n",
      "#Model   0.01s var:14/16 constraints:23/27\n",
      "#Bound   0.01s best:964   next:[965,2165] max_lp\n",
      "#Bound   0.01s best:964   next:[965,1478] reduced_costs\n",
      "#9       0.01s best:965   next:[966,1478] violation_ls(batch:1 #solutions_imported:1 #lin_moves:0 #lin_evals:135 #gen_moves:84 #gen_evals:0 #comp_moves:6 #backtracks:39 #weight_updates:1)\n",
      "#Bound   0.01s best:965   next:[966,1279] reduced_costs\n",
      "#10      0.01s best:967   next:[968,1279] graph_var_lns (d=0.50 s=15 t=0.10 p=0.00 stall=0 h=auto_l0) [presolve]\n",
      "#Model   0.01s var:10/16 constraints:15/27\n",
      "#Bound   0.01s best:967   next:[968,1267] reduced_costs\n",
      "#11      0.01s best:1068  next:[1069,1267] rnd_cst_lns (d=0.50 s=14 t=0.10 p=0.00 stall=0 h=auto_l0)\n",
      "#Bound   0.01s best:1068  next:[1069,1235] reduced_costs\n",
      "#Bound   0.01s best:1068  next:[1069,1120] reduced_costs\n",
      "#12      0.01s best:1088  next:[1089,1120] quick_restart (fixed_bools=42/62)\n",
      "#13      0.01s best:1120  next:[]         reduced_costs (fixed_bools=15/27)\n",
      "#Done    0.01s reduced_costs\n",
      "#Done    0.01s probing\n",
      "#Done    0.01s quick_restart\n",
      "\n",
      "Task timing                          n [     min,      max]      avg      dev     time         n [     min,      max]      avg      dev    dtime\n",
      "                     'core':         1 [  5.57ms,   5.57ms]   5.57ms   0.00ns   5.57ms         1 [ 51.86us,  51.86us]  51.86us   0.00ns  51.86us\n",
      "               'default_lp':         1 [  5.61ms,   5.61ms]   5.61ms   0.00ns   5.61ms         1 [932.51us, 932.51us] 932.51us   0.00ns 932.51us\n",
      "         'feasibility_pump':         1 [173.87us, 173.87us] 173.87us   0.00ns 173.87us         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns\n",
      "          'fj_long_default':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns\n",
      "         'fj_short_default':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns\n",
      "                'fs_random':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns\n",
      "  'fs_random_quick_restart':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns\n",
      "            'graph_arc_lns':         1 [490.85us, 490.85us] 490.85us   0.00ns 490.85us         1 [  1.51us,   1.51us]   1.51us   0.00ns   1.51us\n",
      "            'graph_cst_lns':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns\n",
      "            'graph_dec_lns':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns\n",
      "            'graph_var_lns':         1 [311.49us, 311.49us] 311.49us   0.00ns 311.49us         1 [ 10.00ns,  10.00ns]  10.00ns   0.00ns  10.00ns\n",
      "           'lb_tree_search':         1 [  3.99ms,   3.99ms]   3.99ms   0.00ns   3.99ms         1 [  1.84us,   1.84us]   1.84us   0.00ns   1.84us\n",
      "                   'max_lp':         1 [  5.53ms,   5.53ms]   5.53ms   0.00ns   5.53ms         1 [ 50.06us,  50.06us]  50.06us   0.00ns  50.06us\n",
      "                    'no_lp':         1 [  5.51ms,   5.51ms]   5.51ms   0.00ns   5.51ms         1 [  1.86ms,   1.86ms]   1.86ms   0.00ns   1.86ms\n",
      "      'objective_lb_search':         1 [  2.99ms,   2.99ms]   2.99ms   0.00ns   2.99ms         1 [596.81us, 596.81us] 596.81us   0.00ns 596.81us\n",
      "                  'probing':         1 [  2.85ms,   2.85ms]   2.85ms   0.00ns   2.85ms         1 [ 34.94us,  34.94us]  34.94us   0.00ns  34.94us\n",
      "             'pseudo_costs':         1 [  4.02ms,   4.02ms]   4.02ms   0.00ns   4.02ms         1 [ 15.30us,  15.30us]  15.30us   0.00ns  15.30us\n",
      "            'quick_restart':         1 [  5.31ms,   5.31ms]   5.31ms   0.00ns   5.31ms         1 [919.43us, 919.43us] 919.43us   0.00ns 919.43us\n",
      "      'quick_restart_no_lp':         1 [  4.02ms,   4.02ms]   4.02ms   0.00ns   4.02ms         1 [  1.31ms,   1.31ms]   1.31ms   0.00ns   1.31ms\n",
      "            'reduced_costs':         1 [  5.16ms,   5.16ms]   5.16ms   0.00ns   5.16ms         1 [188.00us, 188.00us] 188.00us   0.00ns 188.00us\n",
      "                'rins/rens':         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns         0 [  0.00ns,   0.00ns]   0.00ns   0.00ns   0.00ns\n",
      "              'rnd_cst_lns':         1 [  1.24ms,   1.24ms]   1.24ms   0.00ns   1.24ms         1 [ 61.89us,  61.89us]  61.89us   0.00ns  61.89us\n",
      "              'rnd_var_lns':         1 [310.15us, 310.15us] 310.15us   0.00ns 310.15us         1 [ 10.00ns,  10.00ns]  10.00ns   0.00ns  10.00ns\n",
      "             'violation_ls':         1 [262.51us, 262.51us] 262.51us   0.00ns 262.51us         1 [ 37.10us,  37.10us]  37.10us   0.00ns  37.10us\n",
      "\n",
      "Search stats                  Bools  Conflicts  Branches  Restarts  BoolPropag  IntegerPropag\n",
      "                     'core':    358        116        40       147       1'413          1'074\n",
      "               'default_lp':     87         54       229        70         973          5'782\n",
      "                'fs_random':      0          0         0         0           0              0\n",
      "  'fs_random_quick_restart':      0          0         0         0           0              0\n",
      "           'lb_tree_search':     14          0        28        28          72            121\n",
      "                   'max_lp':     14          0        28        28          72            122\n",
      "                    'no_lp':    183        170       449        90       2'221         12'992\n",
      "      'objective_lb_search':     24          9        40        31         102          4'503\n",
      "                  'probing':     16          0        32        32          73            101\n",
      "             'pseudo_costs':     14          0        28        28          72            121\n",
      "            'quick_restart':     62         53       311        97       1'221          4'985\n",
      "      'quick_restart_no_lp':     68         83       362        85       1'220          8'276\n",
      "            'reduced_costs':     27          1        45        32          94            681\n",
      "\n",
      "SAT stats                     ClassicMinim  LitRemoved  LitLearned  LitForgotten  Subsumed  MClauses  MDecisions  MLitTrue  MSubsumed  MLitRemoved  MReused\n",
      "                     'core':             0           0           4             0         0         0           0         0          0            0        0\n",
      "               'default_lp':            48          54         240             0         0         4          20         0          0            0        0\n",
      "                'fs_random':             0           0           0             0         0         0           0         0          0            0        0\n",
      "  'fs_random_quick_restart':             0           0           0             0         0         0           0         0          0            0        0\n",
      "           'lb_tree_search':             0           0           0             0         0         0           0         0          0            0        0\n",
      "                   'max_lp':             0           0           0             0         0         0           0         0          0            0        0\n",
      "                    'no_lp':           152         347         620             0         0        16          37        11          0           44        0\n",
      "      'objective_lb_search':             5           5          15             0         0         0           0         0          0            0        0\n",
      "                  'probing':             0           0           0             0         0         0           0         0          0            0        0\n",
      "             'pseudo_costs':             0           0           0             0         0         0           0         0          0            0        0\n",
      "            'quick_restart':            46          77         180             0         0        31         116         0         21           87        0\n",
      "      'quick_restart_no_lp':            41          52         298             0         4        27          79         9         15           93        0\n",
      "            'reduced_costs':             0           0           3             0         0         0           0         0          0            0        0\n",
      "\n",
      "Lp stats                  Component  Iterations  AddedCuts  OPTIMAL  DUAL_F.  DUAL_U.\n",
      "           'default_lp':          1          90          0       80        0        0\n",
      "       'lb_tree_search':          1          26         42        3        0        0\n",
      "               'max_lp':          1          33         53        5        0        0\n",
      "  'objective_lb_search':          1          16          0       23        0        0\n",
      "              'probing':          1          31          0       26        0        0\n",
      "         'pseudo_costs':          1          17         16        7        0        0\n",
      "        'quick_restart':          1          96          0       61        0        0\n",
      "        'reduced_costs':          1          47         35       19        0        0\n",
      "\n",
      "Lp dimension                                                           Final dimension of first component\n",
      "           'default_lp':    11 rows, 8 columns, 25 entries with magnitude in [1.891483e-01, 1.000000e+00]\n",
      "       'lb_tree_search':  39 rows, 16 columns, 103 entries with magnitude in [9.710232e-03, 1.000000e+00]\n",
      "               'max_lp':  51 rows, 16 columns, 160 entries with magnitude in [4.289005e-03, 1.000000e+00]\n",
      "  'objective_lb_search':    10 rows, 8 columns, 23 entries with magnitude in [1.891483e-01, 1.000000e+00]\n",
      "              'probing':    10 rows, 8 columns, 23 entries with magnitude in [1.891483e-01, 1.000000e+00]\n",
      "         'pseudo_costs':   18 rows, 16 columns, 42 entries with magnitude in [3.860974e-02, 1.000000e+00]\n",
      "        'quick_restart':    11 rows, 8 columns, 25 entries with magnitude in [1.891483e-01, 1.000000e+00]\n",
      "        'reduced_costs':   33 rows, 16 columns, 40 entries with magnitude in [1.891483e-01, 1.000000e+00]\n",
      "\n",
      "Lp debug                  CutPropag  CutEqPropag  Adjust  Overflow  Bad  BadScaling\n",
      "           'default_lp':          0            0      24         0  103           0\n",
      "       'lb_tree_search':          0            0       0         0   98           0\n",
      "               'max_lp':          0            0       2         0  119           0\n",
      "  'objective_lb_search':          0            0       7         0   76           0\n",
      "              'probing':          0            0      17         0  287           0\n",
      "         'pseudo_costs':          0            0       0         0   13           0\n",
      "        'quick_restart':          0            0      24         0  160           0\n",
      "        'reduced_costs':          1            1       9         0  194           0\n",
      "\n",
      "Lp pool                   Constraints  Updates  Simplif  Merged  Shortened  Split  Strenghtened  Cuts/Call\n",
      "           'default_lp':           11        8        0       0          0      0             0        0/0\n",
      "       'lb_tree_search':           67        0        0       0          0      0             0      42/75\n",
      "               'max_lp':           78        0        0       0          0      0             0      53/99\n",
      "  'objective_lb_search':           11       14        0       0          0      0             0        0/0\n",
      "              'probing':           11        0        0       0          0      0             0        0/0\n",
      "         'pseudo_costs':           41        0        0       0          0      0             0      16/20\n",
      "        'quick_restart':           11       48        0       0          0      0             0        0/0\n",
      "        'reduced_costs':           60       49      158       0         50      1            19      35/50\n",
      "\n",
      "Lp Cut           max_lp  reduced_costs  pseudo_costs  lb_tree_search\n",
      "         CG_FF:       5              1             -               2\n",
      "          CG_K:       5              -             -               4\n",
      "          CG_R:       4              -             -               3\n",
      "         CG_RB:      11              4             -              10\n",
      "        CG_RBP:       -              1             -               -\n",
      "            IB:       3             21            16               2\n",
      "      MIR_1_FF:       5              2             -               7\n",
      "       MIR_1_K:       4              -             -               3\n",
      "      MIR_1_RB:       4              1             -               2\n",
      "      MIR_2_FF:       3              2             -               3\n",
      "      MIR_2_RB:       3              1             -               3\n",
      "      MIR_3_FF:       1              1             -               2\n",
      "       MIR_3_K:       1              -             -               1\n",
      "      MIR_3_RB:       3              -             -               -\n",
      "      MIR_4_FF:       1              -             -               -\n",
      "  ZERO_HALF_RB:       -              1             -               -\n",
      "\n",
      "LNS stats           Improv/Calls  Closed  Difficulty  TimeLimit\n",
      "  'graph_arc_lns':           0/1    100%        0.71       0.10\n",
      "  'graph_cst_lns':           0/0      0%        0.50       0.10\n",
      "  'graph_dec_lns':           0/0      0%        0.50       0.10\n",
      "  'graph_var_lns':           1/1    100%        0.71       0.10\n",
      "      'rins/rens':           0/0      0%        0.50       0.10\n",
      "    'rnd_cst_lns':           1/1    100%        0.71       0.10\n",
      "    'rnd_var_lns':           0/1    100%        0.71       0.10\n",
      "\n",
      "LS stats               Batches  Restarts  LinMoves  GenMoves  CompoundMoves  WeightUpdates\n",
      "   'fj_long_default':        0         0         0         0              0              0\n",
      "  'fj_short_default':        0         0         0         0              0              0\n",
      "      'violation_ls':        1         0         0        84              6              1\n",
      "\n",
      "Solutions (13)      Num     Rank\n",
      "     'default_lp':    3    [6,8]\n",
      "  'graph_var_lns':    1  [10,10]\n",
      "          'no_lp':    5    [1,5]\n",
      "  'quick_restart':    1  [12,12]\n",
      "  'reduced_costs':    1  [13,13]\n",
      "    'rnd_cst_lns':    1  [11,11]\n",
      "   'violation_ls':    1    [9,9]\n",
      "\n",
      "Objective bounds     Num\n",
      "      'default_lp':    1\n",
      "  'initial_domain':    1\n",
      "          'max_lp':    2\n",
      "   'reduced_costs':    5\n",
      "\n",
      "Solution repositories    Added  Queried  Ignored  Synchro\n",
      "  'feasible solutions':     17        9        0       14\n",
      "        'lp solutions':      0        0        0        0\n",
      "                'pump':      0        0\n",
      "\n",
      "Improving bounds shared    Num\n",
      "            'default_lp':   17\n",
      "                 'no_lp':   23\n",
      "   'objective_lb_search':    9\n",
      "         'quick_restart':   43\n",
      "   'quick_restart_no_lp':   64\n",
      "\n",
      "Clauses shared            Num\n",
      "                'no_lp':    1\n",
      "        'quick_restart':    2\n",
      "  'quick_restart_no_lp':    3\n",
      "\n",
      "CpSolverResponse summary:\n",
      "status: OPTIMAL\n",
      "objective: 1120\n",
      "best_bound: 1120\n",
      "integers: 0\n",
      "booleans: 0\n",
      "conflicts: 0\n",
      "branches: 0\n",
      "propagations: 0\n",
      "integer_propagations: 0\n",
      "restarts: 0\n",
      "lp_iterations: 0\n",
      "walltime: 0.0102963\n",
      "usertime: 0.0102963\n",
      "deterministic_time: 0.00610367\n",
      "gap_integral: 0.00719432\n",
      "solution_fingerprint: 0x213e9d46fe8ac5fa\n",
      "\n",
      "Buy 930 of component 1\n",
      "Buy 1200 of component 2\n",
      "Buy 870 of component 3\n",
      "Produce 210 of product 1\n",
      "Produce 150 of product 2\n",
      "Overall gain: 1120.0\n"
     ]
    }
   ],
   "source": [
    "requirements_1 = (3, 5, 2)\n",
    "requirements_2 = (2, 1, 3)\n",
    "\n",
    "from ortools.sat.python import cp_model\n",
    "\n",
    "model = cp_model.CpModel()\n",
    "buy_1 = model.new_int_var(0, 1_500, \"buy_1\")\n",
    "buy_2 = model.new_int_var(0, 1_500, \"buy_2\")\n",
    "buy_3 = model.new_int_var(0, 1_500, \"buy_3\")\n",
    "\n",
    "produce_1 = model.new_int_var(0, 300, \"produce_1\")\n",
    "produce_2 = model.new_int_var(0, 300, \"produce_2\")\n",
    "\n",
    "model.add(produce_1 * requirements_1[0] + produce_2 * requirements_2[0] <= buy_1)\n",
    "model.add(produce_1 * requirements_1[1] + produce_2 * requirements_2[1] <= buy_2)\n",
    "model.add(produce_1 * requirements_1[2] + produce_2 * requirements_2[2] <= buy_3)\n",
    "\n",
    "from piecewise_functions import PiecewiseLinearFunction, PiecewiseLinearConstraint\n",
    "\n",
    "costs_1 = [(0, 0), (1000, 400), (1500, 1300)]\n",
    "costs_2 = [(0, 0), (300, 300), (700, 500), (1200, 600), (1500, 1100)]\n",
    "costs_3 = [(0, 0), (200, 400), (500, 700), (1000, 900), (1500, 1500)]\n",
    "f_costs_1 = PiecewiseLinearFunction(\n",
    "    xs=[x for x, y in costs_1], ys=[y for x, y in costs_1]\n",
    ")\n",
    "f_costs_2 = PiecewiseLinearFunction(\n",
    "    xs=[x for x, y in costs_2], ys=[y for x, y in costs_2]\n",
    ")\n",
    "f_costs_3 = PiecewiseLinearFunction(\n",
    "    xs=[x for x, y in costs_3], ys=[y for x, y in costs_3]\n",
    ")\n",
    "\n",
    "gain_1 = [(0, 0), (100, 800), (200, 1600), (300, 2_000)]\n",
    "gain_2 = [(0, 0), (80, 1_000), (150, 1_300), (200, 1_400), (300, 1_500)]\n",
    "f_gain_1 = PiecewiseLinearFunction(xs=[x for x, y in gain_1], ys=[y for x, y in gain_1])\n",
    "f_gain_2 = PiecewiseLinearFunction(xs=[x for x, y in gain_2], ys=[y for x, y in gain_2])\n",
    "\n",
    "x_costs_1 = PiecewiseLinearConstraint(model, buy_1, f_costs_1, upper_bound=False)\n",
    "x_costs_2 = PiecewiseLinearConstraint(model, buy_2, f_costs_2, upper_bound=False)\n",
    "x_costs_3 = PiecewiseLinearConstraint(model, buy_3, f_costs_3, upper_bound=False)\n",
    "\n",
    "x_gain_1 = PiecewiseLinearConstraint(model, produce_1, f_gain_1, upper_bound=True)\n",
    "x_gain_2 = PiecewiseLinearConstraint(model, produce_2, f_gain_2, upper_bound=True)\n",
    "\n",
    "model.maximize(x_gain_1.y + x_gain_2.y - (x_costs_1.y + x_costs_2.y + x_costs_3.y))\n",
    "\n",
    "solver = cp_model.CpSolver()\n",
    "solver.parameters.log_search_progress = True\n",
    "status = solver.solve(model)\n",
    "print(f\"Buy {solver.value(buy_1)} of component 1\")\n",
    "print(f\"Buy {solver.value(buy_2)} of component 2\")\n",
    "print(f\"Buy {solver.value(buy_3)} of component 3\")\n",
    "print(f\"Produce {solver.value(produce_1)} of product 1\")\n",
    "print(f\"Produce {solver.value(produce_2)} of product 2\")\n",
    "print(f\"Overall gain: {solver.ObjectiveValue()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_468786/4113396303.py:9: RankWarning: Polyfit may be poorly conditioned\n",
      "  coefficients = np.polyfit(x, y, n)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost_component_1 = [\n",
    "    (0, 0),\n",
    "    (500, 2_000),\n",
    "    (1_000, 3_000),\n",
    "    (1_900, 4_000),\n",
    "    (2_500, 7_000),\n",
    "]\n",
    "cost_component_2 = [\n",
    "    (0, 0),\n",
    "    (300, 2_000),\n",
    "    (900, 3_000),\n",
    "    (1_600, 4_000),\n",
    "    (2_500, 7_000),\n",
    "]\n",
    "cost_component_3 = [\n",
    "    (0, 0),\n",
    "    (700, 2_000),\n",
    "    (1_200, 3_000),\n",
    "    (2_100, 4_000),\n",
    "    (2_500, 7_000),\n",
    "]\n",
    "\n",
    "\n",
    "def plot_cost_component(cost_component, ax, label):\n",
    "    x, y = zip(*cost_component)\n",
    "    n = 5\n",
    "    coefficients = np.polyfit(x, y, n)\n",
    "\n",
    "    # Define the function\n",
    "    def f(x: int) -> float:\n",
    "        return sum(c * x ** (n - i) for i, c in enumerate(coefficients))\n",
    "\n",
    "    x = [i for i in range(2_000)]\n",
    "    y = [f(i) for i in x]\n",
    "    ax.plot(x, y, label=label)\n",
    "    ax.plot(\n",
    "        [x for x in range(0, 2_001, 500)],\n",
    "        [f(x) for x in range(0, 2_001, 500)],\n",
    "        \"-ro\",\n",
    "        label=\"Piecewise Linear Approximation of f(x)\",\n",
    "    )\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.title(\"Approximating a non-linear function\")\n",
    "# plot_cost_component(cost_component_1, ax, 'Component 1')\n",
    "plot_cost_component(cost_component_2, ax, \"Original Function f(x)\")\n",
    "# plot_cost_component(cost_component_3, ax, 'Component 3')\n",
    "ax.set_xlabel(\"Size of bulk order (x)\")\n",
    "ax.set_ylabel(\"Cost of bulk order (f(x))\")\n",
    "# plt.plot([x for x, y in cost_component_1], [y for x, y in cost_component_1], 'bo-')\n",
    "# plt.plot([x for x, y in cost_component_2], [y for x, y in cost_component_2], 'o-', color='orange')\n",
    "# plt.plot([x for x, y in cost_component_3], [y for x, y in cost_component_3], 'go-')\n",
    "\n",
    "plt.grid()\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./pwla.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the points\n",
    "x = np.array([0, 500, 750, 1000])\n",
    "y = np.array([0, 1000, 1100, 1600])\n",
    "\n",
    "# Fit a cubic polynomial to the points\n",
    "coefficients = np.polyfit(x, y, 3)\n",
    "\n",
    "\n",
    "# Define the function\n",
    "def f(x: int) -> float:\n",
    "    return (\n",
    "        coefficients[0] * x**3\n",
    "        + coefficients[1] * x**2\n",
    "        + coefficients[2] * x\n",
    "        + coefficients[3]\n",
    "    )\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot the points\n",
    "x_ = list(range(0, 1000))\n",
    "y_ = [f(i) for i in x_]\n",
    "plt.plot(x_, y_)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mo312",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
